The topic for the trial lecture was “Application of heuristic optimization for planning of forest inventories and forest management”. We congratulate!

Thesis abstract

Airborne LiDAR (Light Detection and Ranging) has become an important remote sensing tool for forest inventory. In the past two decades, this technology has seen a rapid status transition from experimental to operational, mainly driven by the cost saving – precision increasing duality and paralleled by accelerating technological availability. For large-area resource estimation, airborne laser scanning (ALS) has been proposed as a sampling tool. Two-stage model assisted (MA) and two-phase hybrid (HY) estimators have been proposed for this type of survey. This thesis investigated biomass stocks and biomass change estimation using repeated ALS strip sampling survey and national forest inventory field data. Emphasis was on simulative methods to assess the properties of the estimators.

Initially, a method to perform spatially consistent nearest neighbor imputations of forest data was developed (paper I). The method was used to generate spatially explicit forest populations with realistic spatial structure under a prescribed semivariogram model. In addition, the population distribution was controlled by a prescribed histogram. The method was tested in a small forest area (Våler municipality, Norway) using wall-to-wall ALS data, Landsat 7 imagery, and a dense network of field plots that facilitated semivariogram analysis.

In paper II, MA and HY post-stratified estimators were used to estimate above ground
biomass (AGB) stock and change over a period of five years in the southern portion of
Hedmark County, Norway. The reference points in time were 2006 and 2011. A nested poststratification scheme was trialed, combining land cover classes with change classes. Parametric bootstrapping was demonstrated as a simulative alternative to estimate the model uncertainty component in the hybrid estimator.

Finally, in paper III a practical methodology to create realistic artificial forest populations for two points in time was proposed, using multiple sources of empirical data and prescribed parameters. To this end, the method of paper I was combined with a copula approach to model multivariate relationships, preserving the integrity of the multivariate relationship both horizontally (ALS-AGB) and longitudinally between the two points in time. The method ensured that different types of change were proportionally represented in the artificial population. Sampling simulations were performed on a surrogate population tailored to the southern portion of Hedmark County. The simulations closely followed the actual Hedmark survey rather than the theoretical multi-stage sampling design. It was shown that indirect change estimation is prone to large bias. In paper II, HY was found to be a very precise estimator for change. The simulations confirmed its precision, but exposed biases of up to 75% which depreciated the benefits of using ALS in terms of accuracy in most strata. In the absence of a geographical trend in AGB and AGB change, the systematic sampling design had a minimal effect on the sampling variance, and the variance estimators were not always
conservative.